SHF: Small: Design/Technology Co-Optimization and Exploration in Emerging Scaling

SHF：小型：新兴扩展中的设计/技术协同优化和探索

• 批准号: 1718570
• 负责人: David Pan
• 金额: $ 45万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718570&HistoricalAwards=false
• 关键词: SHF; Small; Design; Technology; Co

As the semiconductor industry reaches 10nm/7nm technology nodes and beyond, more innovations will be realized through alternative or emerging scaling such as novel process/material and device engineering to seek equivalent scaling effects. Such scaling demands closer design and technology co-optimization and explorations. This project will seek to develop novel CAD algorithms, tools, and methodologies to bridge the future IC design and technology gaps in the new era of emerging scaling. The proposed research, if successful, will have tremendous impacts on the $350 billion semiconductor industry which is at a critical point, transitioning from conventional scaling to emerging scaling. The PI will work with industry collaborators for technology transfer to benefit the overall industry and society. The highly interdisciplinary nature of this research will be tightly integrated into curriculum development and student training/outreach activities. The technical problems to be addressed include: (1) new sub-resolution directed-self-assembly (DSA) patterning and layout co-optimizations; (2) new mask and layout optimizations for future hybrid lithography technologies considering emerging processes/materials and design requirements; (3) future standard cell and routing architecture exploration; (4) physical design for next-generation nano-devices.

随着半导体行业达到10NM/7NM技术节点及以后，将通过替代或新兴标度（例如新型流程/材料和设备工程）来实现更多的创新，以寻求等效的缩放效果。这样的缩放需要更紧密的设计和技术合作和探索。该项目将寻求开发新颖的CAD算法，工具和方法，以弥合新兴缩放新时代的未来IC设计和技术差距。拟议的研究，如果成功的话，将对在关键时刻的3500亿美元半导体行业产生巨大影响，从传统规模过渡到新兴的规模。 PI将与行业合作者合作进行技术转移，以使整个行业和社会受益。这项研究的高度跨学科性质将紧密整合到课程开发和学生培训/外展活动中。要解决的技术问题包括：（1）新的子分辨率定向自由组装（DSA）模式和布局合作式化； （2）考虑新兴工艺/材料和设计要求，用于未来混合式光刻技术的新面膜和布局优化； （3）未来的标准单元格和路由体系结构探索； （4）下一代纳米驱动器的物理设计。

项目成果

ISOP: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design

• DOI: 10.23919/date56975.2023.10137055
• 发表时间: 2023-04
• 期刊: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
• 作者: Hyunsu Chae;B. Mutnury;Keren Zhu;D. Wallace;D. Winterberg;D. D. Araujo-D.;J. Reddy;Adam R. Klivans;D. Pan

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

• DOI: 10.1109/tcad.2021.3124762
• 发表时间: 2022-10
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Martin Rapp;H. Amrouch;Yibo Lin;Bei Yu;D. Pan;M. Wolf;J. Henkel

LithoGAN: End-to-End Lithography Modeling with Generative Adversarial Networks

• DOI: 10.1145/3316781.3317852
• 发表时间: 2019-06
• 期刊: 2019 56th ACM/IEEE Design Automation Conference (DAC)
• 作者: Wei Ye;M. Alawieh;Yibo Lin;David Z. Pan

LithoROC: Lithography Hotspot Detection with Explicit ROC Optimization

• DOI: 10.1145/3287624.3288746
• 发表时间: 2019-01
• 期刊: 2019 24th Asia and South Pacific Design Automation Conference (ASP-DAC)
• 作者: Wei Ye;Yibo Lin;Meng Li;Qiang Liu;D. Pan

TEMPO: Fast Mask Topography Effect Modeling with Deep Learning

• DOI: 10.1145/3372780.3375565
• 发表时间: 2020-03
• 期刊: Proceedings of the 2020 International Symposium on Physical Design
• 作者: Wei Ye;M. Alawieh;Yuki Watanabe;S. Nojima;Yibo Lin;D. Pan